Abstract
In this paper we present a feature set for Gamma-ray and Background Hadron events automatic classification. We selected the best parameters combination collected by Cherenkov telescopes in order to make a robust Gamma-ray recognition against different signal noise levels using multiple Machine Learning approaches for pattern recognition. We made a comparison of the robustness to noise for four classifiers reaching an accuracy up to \(90.14\%\) in high noise level cases.
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Burgos-Madrigal, A., Ortiz-Esquivel, A.E., Díaz-Hernández, R., Altamirano-Robles, L. (2018). Feature Selection for Automatic Classification of Gamma-Ray and Background Hadron Events with Different Noise Levels. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_15
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DOI: https://doi.org/10.1007/978-3-030-04491-6_15
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